






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class     lowerCamelCase (__lowercase     ):







		'''simple docstring'''



		_snake_case       :       List[Any]					=    4_2
		_snake_case       :       List[Any]					=    4_2



		def __init__(				self							,						_UpperCamelCase							,						_UpperCamelCase  )      ->				Tuple:
							super().__init__()
							self.register_modules(unet=_UpperCamelCase							,						scheduler=_UpperCamelCase  )



		@torch.no_grad()
		def __call__(				self							,						_UpperCamelCase = 1							,						_UpperCamelCase = 5_0							,						_UpperCamelCase = None							,						_UpperCamelCase = "pil"							,						_UpperCamelCase = True							,						**_UpperCamelCase							,						)      ->				Union[Tuple, ImagePipelineOutput]:
							UpperCAmelCase_       : Tuple				=		self.unet.config.sample_size
							UpperCAmelCase_       : int				=		(batch_size, 3, img_size, img_size)

							UpperCAmelCase_       : List[Any]				=		self.unet

							# sample x_0 ~ N(0, sigma_0^2 * I)
							UpperCAmelCase_       : List[str]				=		randn_tensor(_UpperCamelCase							,						generator=_UpperCamelCase							,						device=self.device  ) * self.scheduler.init_noise_sigma

							self.scheduler.set_timesteps(_UpperCamelCase  )

							for t in self.progress_bar(self.scheduler.timesteps  ):
												# here sigma_t == t_i from the paper
												UpperCAmelCase_       : Optional[Any]				=		self.scheduler.schedule[t]
												UpperCAmelCase_       : int				=		self.scheduler.schedule[t - 1] if t > 0 else 0

												# 1. Select temporarily increased noise level sigma_hat
												# 2. Add new noise to move from sample_i to sample_hat
												UpperCAmelCase_       : Dict				=		self.scheduler.add_noise_to_input(_UpperCamelCase							,						_UpperCamelCase							,						generator=_UpperCamelCase  )

												# 3. Predict the noise residual given the noise magnitude `sigma_hat`
												# The model inputs and output are adjusted by following eq. (213) in [1].
												UpperCAmelCase_       : Union[str, Any]				=		(sigma_hat / 2) * model((sample_hat + 1) / 2							,						sigma_hat / 2  ).sample

												# 4. Evaluate dx/dt at sigma_hat
												# 5. Take Euler step from sigma to sigma_prev
												UpperCAmelCase_       : List[Any]				=		self.scheduler.step(_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase  )

												if sigma_prev != 0:
																	# 6. Apply 2nd order correction
																	# The model inputs and output are adjusted by following eq. (213) in [1].
																	UpperCAmelCase_       : Optional[int]				=		(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2							,						sigma_prev / 2  ).sample
																	UpperCAmelCase_       : List[str]				=		self.scheduler.step_correct(
																	    _UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase							,						step_output.prev_sample							,						step_output['derivative']							,						)
												UpperCAmelCase_       : str				=		step_output.prev_sample

							UpperCAmelCase_       : Union[str, Any]				=		(sample / 2 + 0.5).clamp(0							,						1  )
							UpperCAmelCase_       : Union[str, Any]				=		sample.cpu().permute(0							,						2							,						3							,						1  ).numpy()
							if output_type == "pil":
												UpperCAmelCase_       : List[Any]				=		self.numpy_to_pil(_UpperCamelCase  )

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=_UpperCamelCase  )








import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Optional[int]		,    _lowerCamelCase					:     List[Any]		,    _lowerCamelCase					:     Optional[Any])   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  str       =   BertConfig.from_json_file(_lowerCamelCase)
							print(F'Building PyTorch model from configuration: {config}')
							__UpperCamelCase      :  Optional[Any]       =   BertForPreTraining(_lowerCamelCase)

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase)

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}')
							torch.save(model.state_dict()		,    _lowerCamelCase)


if __name__ == "__main__":
		lowercase    :   Union[str, Any]						=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained BERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		lowercase    :   Any						=			parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)